Artificial Intelligence
The MS in Artificial Intelligence program consists of 30 graduate-level semester credit hours, of which 12 are foundation, 9 are concentration, and 9 are elective (including the three options of coursework or project or thesis). A concentration must be declared by admitted students.
The program includes 4 concentrations, in (1) Computer Vision, (2) Machine Learning, (3) Knowledge Management and Reasoning, and (4) Intelligent Interaction. Students must choose one of three options: coursework, MS project, or MS thesis.
The program may be completed entirely on campus, entirely online, or through a combination of on-campus and online courses.
Accelerated Master's Options for Undergraduate Students (4+1 Program)
Accelerated master’s (4+1) programs in the Computer and Information Science (CIS) department allow qualified undergraduate students to seamlessly transition into the department’s graduate programs. These programs will enable students to earn both a bachelor’s and a master’s degree in a reduced timeframe, enhancing their academic experience and providing a cost-effective pathway to advanced degrees.
Students enrolled in this option can take eligible 500-level courses during their junior and senior years, with up to 9 credit hours of such coursework being double-counted toward both degrees. Additionally, another 6 credit hours earned but not applied to the bachelor degree can later be counted toward the master’s degree. Depending on the number of graduate courses taken while working toward the bachelor program, students will need to complete 15-21 credit hours to finish the master’s program after earning their undergraduate degree.
BS in Computer and Information Science (CIS) or Software Engineering (SWE) can advance to MS in CIS, Data Science (DATA), Artificial Intelligence (AI), Software Engineering (SWE) or Cybersecurity and Information Assurance (CIA).
A maximum of 9 credits from combined undergraduate and graduate courses can be double-counted toward both the undergraduate and graduate degrees. This will streamline the process and reduce the total credit load required to complete both degrees. Any 500-level course that is part of the respective master’s program can be selected for double-counting, as shown in the following table. If there is a mismatch in credit hours between the combined course pair, only the smaller number of credits will be counted.
In addition, students may apply up to 6 additional credits of 500-level courses toward their master’s degree, taken during their undergraduate study, though these credits cannot be double-counted. This allows students to make substantial progress toward their graduate degree while still completing their undergraduate requirements. However, the courses of these six additional credits should be listed in the corresponding graduate program.
Applying to the 4+1/Accelerated option is a two-stage process coordinated with both your undergraduate and graduate advising teams. For detailed instructions and application links, please visit the central 4+1 programs webpage.
The following undergraduate programs are approved for the MS-AI 4+1 program:
- BS in Computer Information Science (CIS)
- BS in Software Engineering (SWE)
Requirements
To satisfy the requirements for the MS degree in Artificial Intelligence, all students admitted to the program are expected to complete a minimum of thirty semester hours of graduate coursework, with a cumulative grade point average of B or better. The program of study consists of core courses, concentration courses, and electives with coursework/project/thesis options.
Minimum Grade Requirement in addition to maintaining a minimum cumulative GPA of 3.0 or higher every semester:
- Courses in which grades of C- or below are earned cannot be used to fulfill degree requirements.
- A minimum of a 3.0 cumulative GPA or higher is required at the time of graduation.
| Code | Title | Credit Hours |
|---|---|---|
| Required Core (12 credits): | 12 | |
| Algorithm Analysis and Design 1 | ||
| Software Engineering | ||
| Artificial Intelligence 1 | ||
| Computational Learning 1 | ||
or ECE 579 | Intelligent Systems | |
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Simultaneous credit toward eligible undergraduate majors and MS Artificial Intelligence for students admitted to the 4+1 option. Please see the College's website for admission requirements and program details.
Concentrations
Students must choose one concentration (Computer Vision, Intelligent Interaction, Knowledge Management and Reasoning, Machine Learning) and complete 3 courses (9 credits) from the selected concentration.
Electives and Options
(9 credits): Any course(s) from an MS in AI concentration area(s) outside the student’s selected concentration can be an elective course(s). Additionally, the elective course(s) can be drawn from other CECS and partner college courses by faculty advisor or program director approval (excluding ENGR 500 and ENGR 501). The total number of elective courses should be three, including one of three options: (i) Coursework: taking three elective courses; (ii) Project: taking an MS Project by completing a 1-semester project (through the MS Project course in lieu of an elective) and two additional elective courses, or (iii) Thesis: taking an MS Thesis by completing a 2-semester thesis project (through the MS Thesis course in lieu of two electives) and one additional elective course. It is mandatory that the student select one of these three options.
Option 1: Coursework. This option requires three elective courses from an MS in AI concentration area(s) outside the student’s selected concentration. The minimum requirements for this option are as follows:
- Foundation courses – 12 credit hours
- Concentration courses – 9 credit hours
- Elective courses — 9 credit hours
Option 2: MS Project. This option requires a project report describing the results of an independent study project under the supervision of the advisor. The scope of the research topic for the project should be defined in such a way that a full-time student could complete the requirements for a master’s degree in 24 months or 6 semesters following the completion of course work by regularly scheduling graduate research credits. The minimum requirements for this option are as follows:
- Foundation courses – 12 credit hours
- Concentration courses – 9 credit hours
- Elective courses — 6 credit hours
- Master’s project – 3 credit hours
Option 3: MS Thesis. This option requires a research thesis prepared under the supervision of the advisor. The thesis describes a research investigation and its results. The scope of the research topic for the thesis should be defined in such a way that a full-time student could complete the requirements for a master’s degree in 24 months or 6 semesters following the completion of course work by regularly scheduling graduate research credits. The minimum requirements for this option are as follows:
- Foundation courses – 12 credit hours
- Concentration courses – 9 credit hours
- Elective courses – 3 credit hours
- Master’s Thesis — 6 credit hours
Concentrations
Select one of the following concentrations and complete 3 courses (9 credits) from the selected concentration:
| Code | Title | Credit Hours |
|---|---|---|
| Computer Vision Concentration | ||
| Select 3 courses (9 credits) from the following: | 9 | |
| Computer Graphics and Visual Computing 1 | ||
| Advanced Computer Graphics | ||
| Information Visualization with Parallel Computing 1 | ||
| Advanced Information Visualization and Virtualization | ||
| Engineering in Virtual World | ||
| Pattern Recognition | ||
| Digital Image Processing | ||
| Sel Top:Image Proc/Mach Vision | ||
| Robot Vision | ||
| Pat Rec & Neural Netwks | ||
| Information Visualization | ||
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Students admitted to the 4+1 program may substitute CIS 515 for CIS 451, and CIS 552 for CIS 452.
| Code | Title | Credit Hours |
|---|---|---|
| Intelligent Interaction Concentration | ||
| Select 3 courses (9 credits) from the following: | 9 | |
| Database Systems | ||
| Introduction to Big Data | ||
| Internet of Things and Smart Cities | ||
| Trustworthy Artificial Intelligence 1 | ||
| Advanced Artificial Intelligence | ||
| Computer Game Design and Implementation 1 | ||
| Computer Game Design II 1 | ||
| Edge Computing 1 | ||
| Research Advances in Computational Game Theory and Economics | ||
| Intelligent Vehicle Systems | ||
| Auto Sensors and Actuators | ||
| Intro Robot Syst | ||
| Mobile Robots | ||
| Res.Meth.Human Fctrs/Ergonomic | ||
| Human-Computer Interaction | ||
- 1
Students admitted to the 4+1 program may substitute CIS 582 for CIS 482, CIS 587 for CIS 487, CIS 588 for CIS 488, and CIS 589 for CIS 489.
| Code | Title | Credit Hours |
|---|---|---|
| Knowledge Management and Reasoning Concentration | ||
| Select 3 courses (9 credits) from the following: | 9 | |
| Introduction to Natural Language Processing 1 | ||
| Text Mining and Information Retrieval 1 | ||
| Foundation of Information Security | ||
| Information Visualization with Parallel Computing 1 | ||
| Data Mining | ||
or ECE 537 | Data Mining | |
| Computational Learning 1 | ||
| Trustworthy Artificial Intelligence 1 | ||
| Deep Learning 1 | ||
| Advanced Artificial Intelligence | ||
| Advanced Data Management | ||
| Research Advances in Artificial Intelligence | ||
| Advanced Data Mining | ||
| Analytic and Comp Math | ||
| Probability & Statistical Mod | ||
| Multivariate Statistics | ||
- 1
Students admitted to the 4+1 program may substitute CIS 511 for CIS 411, CIS 536 for CIS 439, CIS 552 for CIS 452, CIS 581 for CIS 481, CIS 582 for CIS 482, and CIS 583 for CIS 483.
| Code | Title | Credit Hours |
|---|---|---|
| Machine Learning Concentration | ||
| Select 3 courses (9 credits) from the following: | 9 | |
| Introduction to Natural Language Processing 1 | ||
| Introduction to Quantum Computing 1 | ||
| Text Mining and Information Retrieval 1 | ||
| Introduction to Big Data | ||
| Computational Learning 1 | ||
| Deep Learning 1 | ||
| Advanced Artificial Intelligence | ||
| Fuzzy Systems | ||
| Stochastic Processes | ||
| Intelligent Systems | ||
| Pat Rec & Neural Netwks | ||
| Adv Intelligent Sys | ||
| Optimization | ||
| Advanced Stochastic Processes | ||
- 1
Students admitted to the 4+1 program may substitute CIS 511 for CIS 411, CIS 512 for CIS 412, CIS 536 for CIS 439, CIS 581 for CIS 481, and CIS 583 for CIS 483.
Leaning Goals
- Understand representations, algorithms and techniques used across works in artificial intelligence and be able to apply and evaluate them in applications as well as develop their own.
- Understand and apply machine-learning techniques, in particular to draw inferences from data and help automate the development of AI systems and components.
- Understand the various ways and reasons humans are integrated into mixed human-AI environments, whether it is to improve overall integrated system performance, improve AI performance or influence human performance and learning.
- Understand the ethical concerns in developing responsible AI technologies.
- Implement AI systems, model human behavior, and evaluate their performance.